DQN [1]} is a relatively typical DRL model inspired by [2]}, where the goal of the agent is to interact with the environment by selecting actions in a way that maximises cumulative future rewards. DQN approximates the optimal action-value function as follows:
\({Q^*(s,a)=\mathbb {E}_{s^{\prime }\sim \epsilon }\left[r+\gamma \max _{a^{\prime }}Q^*(s^{\prime },a^{\prime })|s,a\right]}\) 
